Hypothesis: The transcriptome of subventricular zone derived neural progenitor cells (NPCs) transplanted into a spinal cord injury environment is altered as compared to NPCs transplanted into uninjured spinal cord.
Experimental setup & sequencing: GFP+NPCs were transplanted into injured (250 kdyn contusion injury, 8-10 days post SCI) or uninjured spinal cord using a glas capillary pipette. Lewis female rats were used in all experiments. At 2 & 3 w post transplantation the GFP+NPCs were collected using FACS. FACS of GFP+NPCs from injured spinal cord was performed using injured spinal cord as reference (i.e. negative gate setting). FACS of GFP+NPCs from uninjured spinal cord was performed using uninjured spinal cord as reference. Total RNA, digested of DNase, was isolated and libraries were prepared using SMARTer Stranded Total RNA-Seq Kit - Pico Input Mammalian (Takara). Sequencing was performed 2x125bp in two lane using the HiSeq2500 system and v4 sequencing chemistry (Illumina Inc.) performed by the SNP&SEQ Technology Platform (Stockholm, Sweden).
Data analysis: Data was analysed using edgeR and limma packages, both available through Bioconductor using R (version 3.4.4, Someone to Lean On). Two additional key packages used were ggplot2 and data.table.
Table 1. Data set characteristics.
| Characteristic | Value |
|---|---|
| Samples (n): | 19 |
| Groups (n): | 5 |
| Unique ENSEMBL IDs (n): | 32545 |
Fig 1. Density of log-CPM values pre -and post filtering
Fig 1. Figure reports the density of log-CPM for every sample (by color) pre -and post filtering of genes with low expression. The raw read count matrix is filtered based on log-CPM values. Vertical dashed line represents the cut-off (log-CPM=0, CPM=1). The figure shows a distinct shift of the density from below the threshold (Fig 1A) to above the threshold (Fig 1B). Approximately 1/2 of the genes remain post filtering.
Fig 2. Distribution of log-CPM values pre -and post normalization
Fig 2. Figure reports the distribution of gene expression (log-CPM) for each sample. Fig 2A reports the distribution prior to normalization while Fig 2B reports the distribution following normalization of library sizes using the TMM algorithm. Boxplots are based on all log-CPM values while points represent a random sample of 1e4 genes (due to processing time issues). The difference in the distribution of log-CPM using original and effective library sizes is minor but adjusted for.
Fig 3. Variance explained by principal components based on the 500 genes with highest variance
Fig 3. Figure reports the proportion variance explained by each principal component. Fig 3A reports the proportional variance explained by each component while Fig 3B reports the cumulative variance explained by the components.
Table 2. Upper and lower bounds (bootstrapped 95 % confidence intervals) for the proportion of variance explained by principal component 1 to 10
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Upper bound: | 0.674 | 0.144 | 0.031 | 0.020 | 0.010 | 0.009 | 0.008 | 0.007 | 0.007 | 0.006 |
| Lower bound: | 0.714 | 0.177 | 0.040 | 0.026 | 0.013 | 0.012 | 0.011 | 0.008 | 0.009 | 0.007 |
Fig 4. Dimensionality reduction using PCA and t-SNE of the 500 genes with highest variance
Fig 4. Figure reports the samples in low dimensional space following dimensionality reduction with PCA and t-SNE for the 500 genes with the highest variance. Ellipses are added to the samples for easier recognition of the study groups.
Fig 5. Hierarchical clustering of samples using 500 most variable genes
Fig 5. Figure reports hierarchical clustering based on the 500 most variable genes. Fig 5A reports the clustering using a dendrogram while Fig 5B reports the same clustering using a circular packing plot.
Fig 6. Clustering of samples using common methods following dimensionality reduction with PCA and t-SNE
Fig 6. Figure reports result of K-means clsutering and affinity propagation algorithms implemented on samples following dimensionality reduction using PCA and t-SNE. In the case train-test split was required a 50:50 ratio (9:9 samples) was used instead of a traditional 80:20 ratio due to low number of samples in the test set.
Fig 7. Top ten loadings for the first two principal components
Fig 7. Figure reports the top 10 positive and negative loadings for the first -and second principal component.
Fig 8. Mean-variance relationship pre -and post voom transformation
Fig 8. Figure reports the mean-variance relationship pre -and post application of the voom function. Fig 8A reports the average log-CPM against the quarter root of the variance. Fig 8B reports average log-CPM against the \(log_2(st.dev)\). Blue line reports the average \(log_2(st.dev)\). The red line is a linear trend fitted to the black dots. Each black dot represents a gene. Fig 8A illustrates that the variance is decreasing when the average expression is increasing. In Fig 8B the dependency is removed and the mean variance is unchanged when the average expression increases.
Fig 9. Hierarchical clustering and circular packing plot of 1000 genes with highest F-value
Fig 9. Figure reports hierarchical clustering of samples based on the 1000 genes with highest F-values. Fig 9A reports a dendrogram while Fig 9B reports a circular packing plot.
Fig 10. Number of differentially expressed genes for each contrast (FDR<0.05)
Fig 10. Figure 10A reports the number of DE genes between contrasts Naive[2w] vs invitro, SCI[2w] vs invitro, Naive[2w] vs SCI[2w]. Figure 10B reports the number of DE genes between contrasts Naive[3w] vs invitro, SCI[3w] vs invitro and Naive[3w] vs SCI[3w].
Fig 11. Number of differentially expressed genes for each contrast (FDR<0.05)
Fig 11. Figure reports the number of DE genes within and between contrasts Naive[2w] vs SCI[2w] and Naive[3w] vs SCI[3w].
Table 3. Number of differentially over -and under-expressed genes for each contrast (FDR<0.05)
| Invitro vs Naive[2w] | Invitro vs Naive[3w] | Invitro vs SCI[2w] | Invitro vs SCI[3w] | |
|---|---|---|---|---|
| Downregulated: | 4862 | 4852 | 2411 | 3763 |
| No change | 5236 | 5733 | 9952 | 7574 |
| Upregulated: | 5013 | 4526 | 2748 | 3774 |
| Sum: | 15111 | 15111 | 15111 | 15111 |
| Naive[2w] vs Naive[3w] | Naive[2w] vs SCI[2w] | Naive[2w] vs SCI[3w] | Naive[3w] vs SCI[2w] | Naive[3w] vs SCI[3w] | SCI[2w] vs SCI[3w] | |
|---|---|---|---|---|---|---|
| Downregulated: | 677 | 4401 | 3685 | 3980 | 3203 | 2952 |
| No change | 13994 | 6009 | 7734 | 6441 | 8666 | 9876 |
| Upregulated: | 440 | 4701 | 3692 | 4690 | 3242 | 2283 |
| Sum: | 15111 | 15111 | 15111 | 15111 | 15111 | 15111 |
Fig 12. Mean difference -and volcano plot (FDR<0.05)
Fig 12. Left figure reports a mean-difference plot which illustrates the number of over -and under expressed genes. Threshold is set at \(log_2(fold change)\) +/-1 (blue lines). Blue dots represents genes above or below the log-fold change thresholds while red dots represent those genes which are above/below the thresholds and are significantly (p<0.05) differentially expressed. Right figure is a volcano plot which reports the number of significantly (p<0.05) over -and underexpressed genes (marked with red). Blue dots represent genes which have logFC <-1 or >1 but are not significantly expressed. Figure A&B are for contrast Naive[2w] vs SCI[2w], Figure C&D are for contrast Naive[3w] vs SCI[3w], figure E&F are for contrast Naive[2w] vs Naive[3w] and figure G&H for contrast SCI[2w] vs SCI[3w].
Table 4. 10 most significantly up -and downregulated differentially expressed genes (FDR<0.05)
4A. Contrast: Naive[2w] vs SCI[2w]
| Gene | log2(fold change) | P-value (adjusted) | Gene | log2(fold change) | P-value (adjusted) |
|---|---|---|---|---|---|
| Hapln2 | 8.19 | 1.6e-15 | Gpr37l1 | -5.12 | 3.8e-14 |
| Ptgds | 8.07 | 8.4e-15 | Fads2 | -5.12 | 3.8e-14 |
| Gpr37 | 6.26 | 8.4e-15 | Tubb2b | -4.96 | 8.5e-14 |
| Mal | 9.72 | 3.8e-14 | Limd1 | -4.04 | 8.5e-14 |
| Ldlrap1 | 6.68 | 3.8e-14 | Spry2 | -3.47 | 1.1e-13 |
| Opalin | 9.77 | 4.5e-14 | Gadd45g | -3.89 | 1.4e-13 |
| Tmem63a | 4.23 | 8.1e-14 | Gpm6a | -6.44 | 1.5e-13 |
| Sept4 | 5.54 | 8.5e-14 | Gpr17 | -7.71 | 2.4e-13 |
| Ndrg1 | 5.36 | 8.5e-14 | Cdh2 | -4.37 | 2.9e-13 |
| Dse | 3.82 | 1.1e-13 | Pmel | -6.35 | 2.9e-13 |
4B. Contrast: Naive[3w] vs SCI[3w]
| Gene | log2(fold change) | P-value (adjusted) | Gene | log2(fold change) | P-value (adjusted) |
|---|---|---|---|---|---|
| Hcn2 | 5.07 | 1.5e-13 | Gadd45g | -4.86 | 5.6e-15 |
| Gpr37 | 4.97 | 1.8e-13 | Limd1 | -3.51 | 6.3e-13 |
| Tmem229a | 4.86 | 6.3e-13 | Spry2 | -3.05 | 6.3e-13 |
| LOC361016 | 5.40 | 6.3e-13 | Mdm2 | -3.44 | 1.0e-12 |
| Nkd1 | 4.42 | 6.3e-13 | Slc35e3 | -3.16 | 1.6e-12 |
| Prex2 | 6.20 | 6.3e-13 | Tgfb2 | -5.93 | 3.0e-12 |
| Fbln2 | 6.66 | 6.3e-13 | Pfkfb3 | -2.72 | 7.2e-12 |
| Hapln2 | 5.07 | 7.8e-13 | Shmt2 | -3.40 | 1.2e-11 |
| Ptgds | 5.30 | 1.6e-12 | Os9 | -2.56 | 1.7e-11 |
| Tubb4a | 4.23 | 1.6e-12 | Lrig3 | -2.42 | 3.2e-11 |
4C. Contrast: Naive[2w] vs Naive[3w]
| Gene | log2(fold change) | P-value (adjusted) | Gene | log2(fold change) | P-value (adjusted) |
|---|---|---|---|---|---|
| Chordc1 | 1.02 | 1.3e-03 | Cdh13 | -2.31 | 5.4e-07 |
| LOC102546648 | 1.39 | 1.4e-03 | Flrt1 | -3.37 | 3.1e-05 |
| Ets1 | 1.15 | 2.3e-03 | Rgs7 | -3.57 | 3.1e-05 |
| Zfp217 | 1.29 | 2.3e-03 | Gpr37l1 | -1.48 | 2.6e-04 |
| Gprasp2 | 1.20 | 2.5e-03 | Gpr17 | -2.59 | 2.6e-04 |
| Atp13a5 | 2.82 | 2.6e-03 | Fnd3c2 | -3.79 | 3.3e-04 |
| Myc | 1.09 | 3.7e-03 | Otof | -3.93 | 3.3e-04 |
| P4ha1 | 1.01 | 4.3e-03 | Gria4 | -1.91 | 5.8e-04 |
| LOC679811 | 1.12 | 4.3e-03 | Scn1b | -1.72 | 7.5e-04 |
| Fam46a | 1.13 | 4.4e-03 | Nlgn3 | -1.54 | 7.5e-04 |
4D. Contrast: SCI[2w] vs SCI[3w]
| Gene | log2(fold change) | P-value (adjusted) | Gene | log2(fold change) | P-value (adjusted) |
|---|---|---|---|---|---|
| Fbln2 | 6.05 | 5.2e-11 | Cmklr1 | -3.72 | 5.2e-11 |
| Dhcr24 | 3.48 | 2.7e-10 | Dse | -3.08 | 5.2e-11 |
| Fdft1 | 2.72 | 3.8e-10 | Eng | -3.97 | 5.2e-11 |
| Hmgcs1 | 2.99 | 3.8e-10 | Pgghg | -3.90 | 1.1e-10 |
| Fbn2 | 4.66 | 8.8e-10 | Plin2 | -3.58 | 1.1e-10 |
| Aacs | 2.16 | 1.4e-09 | Nt5e | -3.58 | 1.5e-10 |
| Cyp51 | 2.27 | 1.6e-09 | Acsf2 | -3.07 | 1.6e-10 |
| Acat2 | 3.21 | 1.8e-09 | ENSRNOG00000046171 | -3.58 | 1.7e-10 |
| Fdps | 2.20 | 3.4e-09 | Plxdc2 | -3.13 | 1.9e-10 |
| Epn2 | 1.95 | 4.6e-09 | Lcp1 | -3.28 | 1.9e-10 |
Table 5. GO terms and KEGG pathways (FDR<0.05)
5A. Contrast: Naive[2w] vs SCI[2w]
| Term | Ont | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|---|
| mitochondrial protein complex | CC | 128 | 6 | 95 | 1.0000000 | 0 |
| respiratory chain | CC | 77 | 2 | 66 | 1.0000000 | 0 |
| respiratory chain complex | CC | 70 | 1 | 61 | 1.0000000 | 0 |
| inner mitochondrial membrane protein complex | CC | 106 | 3 | 81 | 1.0000000 | 0 |
| mitochondrial respiratory chain | CC | 69 | 1 | 59 | 1.0000000 | 0 |
| macromolecular complex | CC | 3903 | 960 | 1413 | 1.0000000 | 0 |
| synapse organization | BP | 224 | 40 | 134 | 0.9999997 | 0 |
| mitochondrial membrane part | CC | 177 | 16 | 111 | 1.0000000 | 0 |
| nervous system development | BP | 1914 | 589 | 744 | 0.9442954 | 0 |
| mitochondrial respiratory chain complex I | CC | 42 | 0 | 39 | 1.0000000 | 0 |
| Pathway | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|
| Oxidative phosphorylation | 117 | 3 | 89 | 1.0000000 | 0.0e+00 |
| Parkinson’s disease | 125 | 14 | 92 | 1.0000000 | 0.0e+00 |
| Thermogenesis | 201 | 25 | 118 | 1.0000000 | 0.0e+00 |
| Huntington’s disease | 164 | 27 | 98 | 0.9999999 | 0.0e+00 |
| Alzheimer’s disease | 155 | 30 | 89 | 0.9999874 | 0.0e+00 |
| Retrograde endocannabinoid signaling | 121 | 22 | 71 | 0.9999735 | 0.0e+00 |
| Proteasome | 41 | 3 | 31 | 0.9999917 | 0.0e+00 |
| Non-alcoholic fatty liver disease (NAFLD) | 133 | 36 | 73 | 0.9686994 | 0.0e+00 |
| Cardiac muscle contraction | 58 | 9 | 37 | 0.9996022 | 3.0e-07 |
| RNA transport | 143 | 10 | 71 | 1.0000000 | 2.1e-06 |
5B. Contrast: Naive[3w] vs SCI[3w]
| Term | Ont | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|---|
| RNA binding | MF | 1372 | 120 | 493 | 1 | 0 |
| cytosolic ribosome | CC | 113 | 2 | 75 | 1 | 0 |
| structural constituent of ribosome | MF | 173 | 1 | 94 | 1 | 0 |
| cytosolic large ribosomal subunit | CC | 57 | 0 | 45 | 1 | 0 |
| ribosomal subunit | CC | 176 | 3 | 93 | 1 | 0 |
| translation | BP | 518 | 39 | 204 | 1 | 0 |
| intracellular ribonucleoprotein complex | CC | 709 | 39 | 260 | 1 | 0 |
| ribonucleoprotein complex | CC | 710 | 39 | 260 | 1 | 0 |
| peptide biosynthetic process | BP | 532 | 42 | 205 | 1 | 0 |
| macromolecular complex | CC | 3903 | 610 | 1078 | 1 | 0 |
| Pathway | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|
| Ribosome | 136 | 1 | 82 | 1.0000000 | 0.0000000 |
| Oxidative phosphorylation | 117 | 3 | 51 | 1.0000000 | 0.0000207 |
| Parkinson’s disease | 125 | 12 | 53 | 0.9998333 | 0.0000366 |
| Proteasome | 41 | 0 | 23 | 1.0000000 | 0.0000369 |
| Cell cycle | 117 | 6 | 50 | 0.9999998 | 0.0000473 |
| Spliceosome | 123 | 10 | 49 | 0.9999764 | 0.0004380 |
| Cardiac muscle contraction | 58 | 7 | 27 | 0.9748786 | 0.0005232 |
| RNA transport | 143 | 4 | 54 | 1.0000000 | 0.0010519 |
| Central carbon metabolism in cancer | 55 | 10 | 25 | 0.7524314 | 0.0012824 |
| EGFR tyrosine kinase inhibitor resistance | 75 | 9 | 31 | 0.9863740 | 0.0024128 |
5C. Contrast: Naive[2w] vs Naive[3w]
| Term | Ont | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|---|
| mitochondrial membrane part | CC | 177 | 1 | 36 | 0.9972379 | 0 |
| mitochondrial envelope | CC | 530 | 7 | 66 | 0.9986879 | 0 |
| inner mitochondrial membrane protein complex | CC | 106 | 0 | 27 | 1.0000000 | 0 |
| mitochondrial part | CC | 707 | 11 | 77 | 0.9986088 | 0 |
| mitochondrial membrane | CC | 496 | 7 | 61 | 0.9971251 | 0 |
| mitochondrial protein complex | CC | 128 | 0 | 28 | 1.0000000 | 0 |
| mitochondrial respiratory chain | CC | 69 | 0 | 20 | 1.0000000 | 0 |
| neuron part | CC | 1270 | 38 | 110 | 0.7321345 | 0 |
| respiratory chain complex | CC | 70 | 0 | 20 | 1.0000000 | 0 |
| respiratory chain | CC | 77 | 0 | 20 | 1.0000000 | 0 |
| Pathway | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|
| Oxidative phosphorylation | 117 | 0 | 30 | 1.0000000 | 0.0e+00 |
| Parkinson’s disease | 125 | 1 | 26 | 0.9903783 | 0.0e+00 |
| Thermogenesis | 201 | 4 | 33 | 0.9371245 | 0.0e+00 |
| Alzheimer’s disease | 155 | 3 | 28 | 0.9235928 | 0.0e+00 |
| Huntington’s disease | 164 | 2 | 27 | 0.9838149 | 0.0e+00 |
| Non-alcoholic fatty liver disease (NAFLD) | 133 | 3 | 22 | 0.8649308 | 6.0e-07 |
| Metabolic pathways | 976 | 17 | 79 | 0.9999417 | 3.1e-06 |
| Ribosome | 136 | 0 | 21 | 1.0000000 | 3.2e-06 |
| Cardiac muscle contraction | 58 | 0 | 13 | 1.0000000 | 4.0e-06 |
| Retrograde endocannabinoid signaling | 121 | 3 | 19 | 0.8185828 | 7.4e-06 |
5D. Contrast: SCI[2w] vs SCI[3w]
| Term | Ont | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|---|
| immune system process | BP | 1494 | 162 | 633 | 1.0000000 | 0 |
| immune response | BP | 775 | 75 | 394 | 0.9999999 | 0 |
| regulation of immune system process | BP | 818 | 80 | 379 | 1.0000000 | 0 |
| defense response | BP | 806 | 85 | 372 | 0.9999979 | 0 |
| positive regulation of immune system process | BP | 570 | 38 | 290 | 1.0000000 | 0 |
| response to stimulus | BP | 5466 | 901 | 1488 | 0.0839287 | 0 |
| response to external biotic stimulus | BP | 576 | 47 | 274 | 1.0000000 | 0 |
| response to other organism | BP | 576 | 47 | 274 | 1.0000000 | 0 |
| innate immune response | BP | 366 | 33 | 201 | 0.9999701 | 0 |
| regulation of immune response | BP | 436 | 36 | 225 | 0.9999995 | 0 |
| Pathway | N | Up | Down | P.Up | P.Down |
|---|---|---|---|---|---|
| Osteoclast differentiation | 108 | 8 | 70 | 0.9951665 | 0 |
| NOD-like receptor signaling pathway | 132 | 5 | 79 | 0.9999950 | 0 |
| Lysosome | 107 | 4 | 65 | 0.9999711 | 0 |
| Staphylococcus aureus infection | 29 | 1 | 26 | 0.9914052 | 0 |
| Tuberculosis | 132 | 8 | 72 | 0.9996545 | 0 |
| Leishmaniasis | 53 | 2 | 38 | 0.9982723 | 0 |
| Inflammatory bowel disease (IBD) | 38 | 1 | 30 | 0.9980476 | 0 |
| NF-kappa B signaling pathway | 77 | 5 | 48 | 0.9939415 | 0 |
| Antigen processing and presentation | 54 | 2 | 37 | 0.9985103 | 0 |
| Ribosome | 136 | 0 | 70 | 1.0000000 | 0 |
Fig 13. Hierarchical clustering of samples together with heatmap of significantly differentially expressed genes
13A. Contrast: Naive[2w] vs SCI[2w] (FDR<1e-6)
13B. Contrast: Naive[3w] vs SCI[3w] (FDR<1e-6)
13C. Contrast: Naive[2w] vs Naive[3w] (FDR<0.05)
13D. Contrast: SCI[2w] vs SCI[3w] (FDR<1e-6)
Fig 13. Figure reports a heatmap with hierarchical clustering (indicated with dendrograms) using log-CPM values. Only significantly differentially expressed genes are included (genes with NA symbols were removed).
1. Annotation: Genes are annotated with gene name using their respective ENSEMBL ID.
2. Transformation: Read count matrix is transformed into log-CPM using original library sizes.
3. Filtering: Read count matrix is filtered using log-CPM values (>0 for at least 3 samples).
4. Normalization: Effective library sizes are calculated using the library sizes for the filtered read count matrix and the trimmed mean of M values (TMM) approach.
5. Transformation: Filtered read count matrix is transformed into log-CPM matrix.
6. PCA: Conducted for the 500 genes with highest variance. Proportional variance explained, MDS and loading plots are created.
7. Design matrix: A dummy matrix which indicates which group each sample belongs.
8. Contrast matrix: Contrasts are the group comparisons of interest.
9. Voom transformation: Estimate precision weights for linear modelling to remove dependency between the variance and trhe mean.
10. Linear modelling: Linear modelling using precision weights followed by an empirical Bayes moderation.
11. Differentially expressed genes: Moderated t-statistics are used for determining significantly expressed genes for each contrast. Results are displayed with venn diagrams, mean-difference -and volcano plot and a summary table.
12. Analysis/interpretation: Using hierarchical clustering, heatmap, gene ontology and KEGG enrichment analysis and gene set analysis the difference between the study groups is sought for.
[1] R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL htts://www.R-project.
[2] Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.
[3] Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140
[4] Law CW, Alhamdoosh M, Su S et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 1; referees: 3 approved]. F1000Research 2016, 5:1408.
This analysis was conducted on:
sessionInfo()
## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=sv_SE.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=sv_SE.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=sv_SE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] circlize_0.4.3
## [2] ape_5.0
## [3] igraph_1.2.1
## [4] ggraph_1.0.1
## [5] e1071_1.6-8
## [6] apcluster_1.4.5
## [7] MeanShift_1.1-1
## [8] wavethresh_4.6.8
## [9] EMCluster_0.2-10
## [10] Matrix_1.2-11
## [11] kernlab_0.9-25
## [12] mda_0.4-10
## [13] class_7.3-14
## [14] MASS_7.3-47
## [15] Rtsne_0.13
## [16] caret_6.0-79
## [17] lattice_0.20-35
## [18] xml2_1.2.0
## [19] XML_3.98-1.3
## [20] RCurl_1.95-4.10
## [21] bitops_1.0-6
## [22] pander_0.6.1
## [23] knitr_1.20
## [24] pvclust_2.0-0
## [25] boot_1.3-20
## [26] rafalib_1.0.0
## [27] ggrepel_0.7.0
## [28] plot3D_1.1.1
## [29] ellipse_0.4.1
## [30] VennDiagram_1.6.20
## [31] futile.logger_1.4.3
## [32] gplots_3.0.1
## [33] RColorBrewer_1.1-2
## [34] colorspace_1.3-2
## [35] gridExtra_2.3
## [36] cowplot_0.9.2
## [37] ggplot2_2.2.1.9000
## [38] data.table_1.10.4-3
## [39] Rattus.norvegicus_1.3.1
## [40] TxDb.Rnorvegicus.UCSC.rn5.refGene_3.4.1
## [41] org.Rn.eg.db_3.4.1
## [42] GO.db_3.4.1
## [43] OrganismDbi_1.18.1
## [44] GenomicFeatures_1.28.5
## [45] GenomicRanges_1.28.6
## [46] GenomeInfoDb_1.12.3
## [47] AnnotationDbi_1.38.2
## [48] IRanges_2.10.5
## [49] S4Vectors_0.14.7
## [50] Biobase_2.36.2
## [51] BiocGenerics_0.22.1
## [52] edgeR_3.18.1
## [53] limma_3.32.10
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## loaded via a namespace (and not attached):
## [1] backports_1.1.2 plyr_1.8.4
## [3] lazyeval_0.2.1 splines_3.4.4
## [5] BiocParallel_1.10.1 digest_0.6.15
## [7] foreach_1.4.4 BiocInstaller_1.26.1
## [9] htmltools_0.3.6 viridis_0.5.1
## [11] gdata_2.18.0 magrittr_1.5
## [13] memoise_1.1.0 sfsmisc_1.1-2
## [15] recipes_0.1.2 Biostrings_2.44.2
## [17] gower_0.1.2 dimRed_0.1.0
## [19] matrixStats_0.53.1 blob_1.1.1
## [21] dplyr_0.7.4 graph_1.54.0
## [23] bindr_0.1.1 survival_2.41-3
## [25] iterators_1.0.9 glue_1.2.0
## [27] DRR_0.0.3 gtable_0.2.0
## [29] ipred_0.9-6 zlibbioc_1.22.0
## [31] XVector_0.16.0 DelayedArray_0.2.7
## [33] ddalpha_1.3.1.1 shape_1.4.4
## [35] DEoptimR_1.0-8 scales_0.5.0.9000
## [37] futile.options_1.0.0 DBI_0.8
## [39] Rcpp_0.12.16 viridisLite_0.3.0
## [41] units_0.5-1 foreign_0.8-69
## [43] bit_1.1-12 lava_1.6.1
## [45] prodlim_1.6.1 pkgconfig_2.0.1
## [47] nnet_7.3-12 locfit_1.5-9.1
## [49] labeling_0.3 tidyselect_0.2.4
## [51] rlang_0.2.0.9000 reshape2_1.4.3
## [53] munsell_0.4.3 tools_3.4.4
## [55] RSQLite_2.1.0 broom_0.4.4
## [57] evaluate_0.10.1 stringr_1.3.0
## [59] yaml_2.1.18 ModelMetrics_1.1.0
## [61] bit64_0.9-7 robustbase_0.92-8
## [63] caTools_1.17.1 purrr_0.2.4
## [65] bindrcpp_0.2.2 RBGL_1.52.0
## [67] nlme_3.1-131 RcppRoll_0.2.2
## [69] biomaRt_2.32.1 compiler_3.4.4
## [71] tweenr_0.1.5 tibble_1.4.2
## [73] stringi_1.1.7 highr_0.6
## [75] psych_1.7.8 pillar_1.2.1
## [77] GlobalOptions_0.0.13 rtracklayer_1.36.6
## [79] R6_2.2.2 KernSmooth_2.23-15
## [81] codetools_0.2-15 lambda.r_1.2
## [83] gtools_3.5.0 assertthat_0.2.0
## [85] CVST_0.2-1 SummarizedExperiment_1.6.5
## [87] rprojroot_1.3-2 withr_2.1.2
## [89] GenomicAlignments_1.12.2 Rsamtools_1.28.0
## [91] mnormt_1.5-5 GenomeInfoDbData_0.99.0
## [93] udunits2_0.13 rpart_4.1-11
## [95] timeDate_3043.102 tidyr_0.8.0
## [97] rmarkdown_1.9 misc3d_0.8-4
## [99] ggforce_0.1.1 lubridate_1.7.3